1. Field of the Invention
The present invention relates to an apparatus and a method for steering a vehicle, and more particularly to an apparatus and a method for steering an autonomous vehicle.
2. Description of the Related Art
UGVs (unmanned ground vehicles) are employed in various military and civil applications, which often require UGVs to move autonomously in unknown environments with dynamic and physical constraints, rather than to simply follow a pre-planned path designated by an offline mission-level planning algorithm.
This application is related to an active steering problem of a UGV in complex environments. For the dynamic trajectory generation problem with obstacle avoidance, a model predictive method based on successive online optimal control is employed. In order to use this approach for the UGV, a bicycle model may be used combined with a nonlinear tire-force model to predict the future evolution of the system.
Much research on local obstacle avoidance has been performed, most of which use reactive methods based on sensor data. Some take into account the simple robot dynamics in terms of velocity or turning radius. These approaches are computationally efficient, but the vehicle can get stuck in local minima, sometimes the discretization of the world is required, or incorporation of the complicated dynamics is not easy. A search was used on a trajectory tree of a fixed depth, the dimension of the vehicle was considered with a reduced dynamic window. But as will be shown later, consideration of the location of center of gravity of the vehicle may not suffice for obstacle avoidance in a cramped area.
Recently, predictive active steering control for autonomous vehicle systems was studied, with a tire model, where the autonomous vehicle was directed to follow the given reference which is assumed to be collision-free and achievable by the vehicle.
However, in the case of unknown environments, it is difficult to acquire such a safe pre-defined reference. In addition, because the dimension of the vehicle is not explicitly considered, if the environment is complex and cluttered, it can be a serious issue. Also the previous approach might be limited in the implementation point of view. For real implementation onto UGVs, each optimization should be performed within the given sampling time, which is often in the order of 10 ms or shorter.